Efficiently create sparse pivot tables in pandas?

The answer posted previously by @khammel was useful, but unfortunately no longer works due to changes in pandas and Python. The following should produce the same output:

from scipy.sparse import csr_matrix
from pandas.api.types import CategoricalDtype

person_c = CategoricalDtype(sorted(frame.person.unique()), ordered=True)
thing_c = CategoricalDtype(sorted(frame.thing.unique()), ordered=True)

row = frame.person.astype(person_c).cat.codes
col = frame.thing.astype(thing_c).cat.codes
sparse_matrix = csr_matrix((frame["count"], (row, col)), \
                           shape=(person_c.categories.size, thing_c.categories.size))

>>> sparse_matrix
<3x4 sparse matrix of type '<class 'numpy.int64'>'
     with 6 stored elements in Compressed Sparse Row format>

>>> sparse_matrix.todense()
matrix([[0, 1, 0, 1],
        [1, 0, 0, 1],
        [1, 0, 1, 0]], dtype=int64)


dfs = pd.SparseDataFrame(sparse_matrix, \
                         index=person_c.categories, \
                         columns=thing_c.categories, \
                         default_fill_value=0)
>>> dfs
        a   b   c   d
 him    0   1   0   1
  me    1   0   0   1
 you    1   0   1   0

The main changes were:

• .astype() no longer accepts "categorical". You have to create a CategoricalDtype object.
• sort() doesn't work anymore

Other changes were more superficial:

• using the category sizes instead of a length of the uniqued Series objects, just because I didn't want to make another object unnecessarily
• the data input for the csr_matrix (frame["count"]) doesn't need to be a list object
• pandas SparseDataFrame accepts a scipy.sparse object directly now

Here is a method that creates a sparse scipy matrix based on data and indices of person and thing. person_u and thing_u are lists representing the unique entries for your rows and columns of pivot you want to create. Note: this assumes that your count column already has the value you want in it.

from scipy.sparse import csr_matrix

person_u = list(sort(frame.person.unique()))
thing_u = list(sort(frame.thing.unique()))

data = frame['count'].tolist()
row = frame.person.astype('category', categories=person_u).cat.codes
col = frame.thing.astype('category', categories=thing_u).cat.codes
sparse_matrix = csr_matrix((data, (row, col)), shape=(len(person_u), len(thing_u)))

>>> sparse_matrix 
<3x4 sparse matrix of type '<type 'numpy.int64'>'
    with 6 stored elements in Compressed Sparse Row format>

>>> sparse_matrix.todense()

matrix([[0, 1, 0, 1],
        [1, 0, 0, 1],
        [1, 0, 1, 0]])

Based on your original question, the scipy sparse matrix should be sufficient for your needs, but should you wish to have a sparse dataframe you can do the following:

dfs=pd.SparseDataFrame([ pd.SparseSeries(sparse_matrix[i].toarray().ravel(), fill_value=0) 
                              for i in np.arange(sparse_matrix.shape[0]) ], index=person_u, columns=thing_u, default_fill_value=0)

>>> dfs
     a  b  c  d
him  0  1  0  1
me   1  0  0  1
you  1  0  1  0

>>> type(dfs)
pandas.sparse.frame.SparseDataFrame

I had a similar problem and I stumbled over this post. The only difference was that that I had two columns in the DataFrame that define the "row dimension" (i) of the output matrix. I thought this might be an interesting generalisation, I used the grouper:

# function
import pandas as pd

from scipy.sparse import csr_matrix

def df_to_sm(data, vars_i, vars_j):
    grpr_i = data.groupby(vars_i).grouper

    idx_i = grpr_i.group_info[0]

    grpr_j = data.groupby(vars_j).grouper

    idx_j = grpr_j.group_info[0]

    data_sm = csr_matrix((data['val'].values, (idx_i, idx_j)),
                         shape=(grpr_i.ngroups, grpr_j.ngroups))

    return data_sm, grpr_i, grpr_j


# example
data = pd.DataFrame({'var_i_1' : ['a1', 'a1', 'a1', 'a2', 'a2', 'a3'],
                     'var_i_2' : ['b2', 'b1', 'b1', 'b1', 'b1', 'b4'],
                     'var_j_1' : ['c2', 'c3', 'c2', 'c1', 'c2', 'c3'],
                     'val' : [1, 2, 3, 4, 5, 6]})

data_sm, _, _ = df_to_sm(data, ['var_i_1', 'var_i_2'], ['var_j_1'])

data_sm.todense()